Background: Artificial intelligence in neonatal medicine has produced a decade of published predictive models for sepsis, necrotising enterocolitis, apnoea, and neurodevelopmental outcomes, many with strong accuracy metrics. Bedside implementation remains rare. The gap between algorithmic performance and clinical adoption is not primarily a scientific problem. It is a translation problem. Objectives: To identify the primary barriers preventing validated AI tools from reaching neonatal clinical practice and propose a structured framework for translation. Methods: Four barriers explain most implementation failures. Most AI models are trained on retrospective datasets that do not reflect real-time signal variability in active NICU environments. Validation studies rarely address workflow integration, how an alert is received, interpreted, and acted upon under the constraints of a high-acuity unit. Interface design is treated as secondary , despite alert fatigue and poor visual hierarchy being established causes of clinician non-compliance. Results: Regulatory pathways for AI as a medical device remain poorly understood by teamswithout dedicated product and commercialization expertise. Technically sound models fail not because the science is wrong but because the route from research to clinical practice has not been engineered. Conclusions: Translating AI into neonatal clinical practice requires clinical rigour in model development and product discipline in equal measure. Neither is sufficient alone. Progress depends on collaboration between clinicians, product architects, and implementation scientists. Though the field has accumulated evidence, it lacks the infrastructure framework to deliver it to the bedside.